In this work, a new approach for fault diagnosis in the field of additive manufacturing (3d printing) using artificial intelligence will be given. This approach is based on the marriage of the Bayesian Networks theory and data acquisition techniques. Bayesian Networks are well known for their ability to infer probabilities and to give decisional support under uncertainty. In order to do so, these probability engines must be constructed and maintained by a big amount of data and information using learning algorithms. This work provides a methodology that uses sensors based data acquisition and processing to construct such networks. Some of these sensors are already available in most of the 3d printers available in the market, while other sensors were additionally embedded in a studied 3d printer in order to enrich the number of observational variables to gain a high level of fault diagnosis accuracy and support.
In this work, a Bayesian Networks based fault diagnosis system for industrial machines is proposed. For this purpose, an experimental setup of a CNC machine is given as a test rig. This fault diagnosis system is composed of three levels: The first level concerns a set of sensors that are connected directly to the machine's main organs. The second level is a microcontroller based data acquisition interface that calibrates and transfers the measured data to the third level. The last level is a set of machine learning algorithms that are executed in a computer. These algorithms perform BN structure learning and exploit this structure for classifying the new arrival data from the CNC machine and determining if it presents a faulty or a normal situation.
The present article describes a contribution to solve transportation problems with green constraints. The aim is to solve an urban traveling salesman problem where the objective function is the total emitted CO2. We start by adapting ASIF approach for calculating CO2 emissions to the urban logistics problem. Then, we solve it using ant colony optimization metaheuristic. The problem formulation and solving will both work under a web-based mapping platform. The selected problem is a real-world NP-hard transportation problem in the city of Casablanca.
This paper presents a DC motor fault diagnosis system based on Bayesian networks. This was done by the design of a new electromechanical test bed allowing the collection of functioning data from a real world industrial Direct current (DC) Motor. The data collection will help in the construction of Bayesian networks models. These data are collected from sensors measuring different types of variables that are directly related to the industrial system. Without doing any mathematical modeling that describes the physical properties of the studied DC motor, the proposed tool provides with the help of Bayesian networks parameters and structure learning algorithms, the base to construct a fault diagnosis tool that can be extended to a fault prognosis tool.
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